Behavior and characterization of titanium dioxide and silver nanoparticles in soils
Bibliographic record
Abstract
The presence and transport of emerging Engineered Nano Particles (ENPs) in the environment is driven by combination of multiple factors comprising their size, charge and aggregation/agglomeration rate along with interactions with different soil types. Due to the complexity of the soil, it is difficult to associate an exact concentration with the possible transport pathways, interactions and transformation mechanisms. Major uncertainties arise with the increased number of extraction and filtration steps required for determining the exact toxicity doses of ENPs. Due to these issues, TiO2 and Ag behavior, characterization, transport, and environmental effects in soils are still not clear. In soils, TiO2 and Ag have been mainly reported to be present in the surroundings of point sources and are driven by their aggregation/agglomeration rate in combination with different soil types. TiO2 and Ag are mainly transported by interstitial water depending on their zeta-potential in the local soil. Along the transport route, TiO2 and Ag undergo alteration in dissolution, corrosion, redox reaction and coatings with the soil matrix. Their mobility is better across mineral soil in comparison to soil rich in organic colloids. The bioavailability gets modified and, in consequence, they are retained until complete degradation of the organic matrix. Depending on the soil matrix composition in terms of water content, minerals, and biological structure, the current most used methods for TiO2 and Ag characterization are FFFF and UV spectroscopy coupled with ICP-MS and LCMS/MS. The increased flux of TiO2 and Ag across soil is significant in understanding/accessing the viable threats, in particular their release affects the natural ecosystem.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".